• Provide an overview of PsychoPy2
  • What is it?
  • How does it work?

• Why you should care
  • Gain better understanding of psycholinguistic experiments

• What will you learn?
  • You will be able to create your own simple experiment
  • You will have the resources to create more complicated experiments

• Overview of PsychoPy2
• Demo
• Using PsychoPy2 - the interface
• Creating a simple experiment - Stroop task
• Dealing with results
• Concluding ideas

• What is it?
  • PsychoPy2 is a free program made in Python
  • It can be used to create experiments in psychology, psychophysics, neuroscience and linguistics.

• Why do you want it?
  • It's free!
  • It's cross-platform
  • It has a GUI (builder mode) or you can write in code

• What is it good for?
  • PsychoPy2 can be used to create psycholinguistic tasks:
    • 2AFC
    • AX, AXB, ABX
    • Lexical decision (masked prime)
    • even production experiments

• What is it not good for?
  • ?

• Download latest version of PsychoPy2
  • http://sourceforge.net/projects/psychpy/files/PsychoPy/
  • You can also google 'psychopy'
  • Choose 'standalone' version

• Download workshop files
  • http://www.jvcasillas.com/teaching/psychopy/

• Balloon Analogue Risk Task (BART) (Lejuez et al., 2002)
• The measure is designed to quantify individual differences in risk-taking.

• Cognitive task related to mental representations of objects (Gray & Pasmanter, 2013)

• 2AFC
• Interested in perception of non-native vowel contrasts
  • What acoustic cues do late-learners of English use to perceive tense/lax contrasts?
  • Spectrum? Duration?
• /i/-/ɪ/

• 11-step spectrum continuum
• 11-step duration continuum

import itertools

def iter_primes():
     # an iterator of all numbers between 2 and +infinity
     numbers = itertools.count(2)

     # generate primes forever
     while True:
         # get the first number from the iterator
         prime = numbers.next()
         yield prime
for p in iter_primes():
    if p > 1000:
        break
    print p

• Provides a graphical user interface
• Linear representation of experiment 'flow'
• Row view of routine components

• The experiment advances linearly from left to right
• Each element (a routine) finishes before advancing
• Similar to slides in a power point

• Individual view of each 'slide'
• Comprised of a series of components that perform a task

• Stimuli
  • Text/Audio/Images/Video
• Responses
  • Keyboard, lykert scales, button boxes, mouse tracking, eye tracking, microphone
• Custom
  • Python code

• Decide what you want to do
• Find a similar template
• Choose variables
• Create loops

1. Create a new folder 'stroop' on your desktop
2. Launch Pyschopy2
   • Open new builder window
   • Save new experiment as 'stroop.psyexp' in the 'stroop' folder
3. Select new keyboard response component

1. Create a new folder 'stroop' on your desktop
2. Launch Pyschopy2/Open new builder window
3. Select new keyboard response component
4. Select new text stimuli component

1. Create a new folder 'stroop' on your desktop
2. Launch Pyschopy2/Open new builder window
3. Select new keyboard response component
4. Select new text stimuli component
5. Create condition file
   • Open excel (or text editor)
   • Create 'text' column
   • Create 'color' column
   • Create 'corrAns' column
   • Create 'congruent' column

1. Create a new folder 'stroop' on your desktop
2. Launch Pyschopy2/Open new builder window
3. Select new keyboard response component
4. Select new text stimuli component
5. Create condition file
6. Save condition file in 'stroop' folder, create loop

1. Create a new folder 'stroop' on your desktop
2. Launch Pyschopy2/Open new builder window
3. Select new keyboard response component
4. Select new text stimuli component
5. Create condition file
6. Save condition file in 'stroop' folder, create loop
7. Run experiment

• You made an awesome experiment, collected data… now what?

• Specify the output format
• Import results directly into stats program

# Set working directory
setwd("~/google_drive/14_fall_2014/proseminar/psychopy2_workshop")

# Combine files vertically into large data frame
temp <- list.files(path = "./demos/3_stroop/data", full.names = TRUE, pattern = ".csv")
myfiles <- lapply(temp, read.csv,sep = ",")
df <- do.call("rbind",myfiles)

mod <- glm(resp.corr ~ letterColor, data = df, family = "binomial")

• You now know the basics of Pyschopy2
  • Can be used to create simple psycholinguistic experiments
  • Provided templates can be modified to create more complex experiments

• Pros
  • Free
  • Allows for control over everything
  • Easy to streamline into research workflow
• Cons
  • Complicated?
  • Ugly?

• http://www.jvcasillas.com/teaching/psychopy/
• http://www.psychopy.org/
• http://code.google.com/p/psychopy/
• http://www.youtube.com/watch?v=VV6qhuQgsiI

• Gray, J. & Pasmanter, N. (2013). github
• Lejuez, C. W., Aklin, W. M., Zvolensky, M. J., & Pedulla, C. M. (2003). Evaluation of the Balloon Analogue Risk Task (BART) as a predictor of adolescent real-world risk-taking behaviours. Journal of adolescence, 26(4), 475-479.
• McGuire, G. (2010, in progress) A Brief Primer on Experimental Designs in Speech Perception Research. http://people.ucsc.edu/~gmcguir1/
• Simonet, M. (2012). El diseño de experimentos para el estudio de la percepción del habla. Laboratory Approaches to Romance Phonology Conference. El Colegio de México, México D.F.